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Flash

first_img Castle Labs: Variational Swaps have execution costs 8 to 12 times lower than mainstream on-chain venues

Castle Labs released a research report on September 10, comparing the execution costs of the swaps products launched by Variational with traditional perpetual contracts. The report shows that for most trading volumes, Variational is currently the lowest-cost venue among listed assets, with the cost of a $1 million trade in the US100 market being only $47.The report points out that the trading volume of RWA perpetual contracts has grown from less than $1 billion in October 2025 to over $12 billion in August 2026, currently accounting for about 12% to 13% of on-chain perpetual contract trading volume, with a peak in July reaching 20%. As of the report's release, the total open interest of RWA perpetual contracts was $4.9 billion, with TradeXYZ and Variational accounting for nearly 90%.Variational's swaps utilize RFQ execution and the proprietary liquidity provider Omni, with liquidity coming directly from traditional financial partners, and the fees being a holding cost charged once at the daily close, rather than relying on market supply and demand funding rates. Since the launch of the US100, US500, XAU, XAG, and USOIL markets at the beginning of the month, a total trading volume of $3.8 billion has been accumulated, with a peak open interest of $245 million. Currently, swaps have contributed over 50% of Variational's daily trading volume and more than $220 million in open interest.

Bitget CFD Chief Analyst: The real risk of PPI is not the increase, but the restart of cost transmission

This week's upcoming release of the U.S. Producer Price Index (PPI) has become a key point for judging whether inflation is re-accelerating. Bitget CFD Chief Analyst Lewis Huang pointed out in a live broadcast that, against the backdrop of 162,000 new non-farm jobs and an unemployment rate holding steady at 4.1%, the demand side in the U.S. remains resilient. If core PPI and service prices continue to stay high, companies may pass costs onto consumers, driving up subsequent CPI and prompting the market to reprice the Federal Reserve's policy path of "maintaining high interest rates for a longer period."Lewis Huang further analyzed two scenarios: if PPI exceeds expectations but CPI remains moderate, it indicates that companies lack pricing power and can only compress profit margins to absorb costs; if both PPI and CPI exceed expectations, it signifies that the inflation transmission chain has reopened, potentially serving as a catalyst for a stronger dollar and U.S. Treasury yields. From a trading perspective, if PPI exceeds expectations and drives the dollar up, gold and high-valuation tech indices like the Nasdaq 100 may come under pressure; conversely, if PPI falls short of expectations and the dollar retreats, it would support a rebound in gold and growth stock indices.Lewis Huang reminded traders that they should not only focus on the first wave of market movements following the data release but also observe whether PPI is confirmed by CPI, the dollar, and U.S. Treasury yields. If the rise in PPI is merely a short-term cost shock, the market impact will be limited; if costs continue to be transmitted to consumers, the market narrative may shift back to "recurring inflation and the continuation of high interest rates."

RootData: Apple, QQQ, and over ten popular assets have the most optimal trading costs on Bitget, with a weighted price difference of 0.0144%

According to the report "Explosive Growth of Stock Derivatives in 2026: The Landscape of Cryptocurrency Exchanges and Key Trends" released by RootData, the stock derivatives sector has transitioned from "marginal experimentation" to the "explosive growth" phase, with a cumulative trading volume of approximately $17.5 trillion from January to August.In terms of cumulative transaction volume, the concentration effect among the top exchanges remains significant. Among the four exchanges, Binance ranks first with $853.58 billion and a 61.3% market share; Bitget follows in second place with $270.85 billion and a 19.5% market share; OKX comes in third with $234.39 billion and a 16.8% market share; Bybit ranks fourth with $33.41 billion and a 2.4% market share.Regarding liquidity, in the ±2% weighted order book depth indicator, Binance and Bitget together account for over 70% of the stock derivatives order book liquidity. Among them, Binance has an average daily order book depth of approximately $10.1 million, followed closely by Bitget at $4.82 million, with OKX and Bybit at $3.87 million and $1.16 million, respectively.In terms of trading costs, in the recent comparison of weighted spreads for more than a dozen representative popular assets, Bitget ranks first with 0.0144%, followed closely by Binance at 0.0145%, with both essentially at the same level; OKX is at 0.0154%, and Bybit is at 0.0237%.

Anthropic signed at least 14.8GW of computing power in the past 11 months, with a potential cost of up to 517 billion USD

According to statistics from The Information, Anthropic has signed at least 14.8GW of computing power in the past 11 months, which can be gradually utilized in the coming years. Based on currently public contracts, the potential total cost could reach up to $517 billion, with most expenditures occurring over the next decade. In addition to the 1-2GW already secured before October last year, the total computing power signed by Anthropic is approximately 16GW.This round of expansion is primarily driven by the demand for Claude. This year, the growth of Claude Code and Cowork has exceeded Anthropic's expectations, prompting the company to focus on acquiring computing power. The new agreement with Amazon provides up to 5GW, while Google and Broadcom offer another 5GW, and Microsoft and NVIDIA provide approximately 1GW. Anthropic has also rented all computing power from SpaceX's Colossus 1, acquiring over 220,000 NVIDIA GPUs, including H100, H200, and GB200.Anthropic has secured about 16GW, with many contracts extending beyond 2030. OpenAI has set a target for investors to reach 30GW by 2030, expecting to invest approximately $750 billion in computing power by that year. The $517 billion figure is the potential maximum cost estimated by The Information based on existing cloud services, chip, and data center contracts, with some computing power potentially being delayed or unused, and some contracts allowing for early cancellation.

first_img Tesla Cybercab launched in the United States, with no steering wheel and pedals costing 0.84 yuan per kilometer

On September 3 local time, Tesla held a Cybercab launch event in Austin, Texas, USA. This vehicle is a mass-produced model designed for autonomous driving scenarios, eliminating the steering wheel, pedals, and traditional rearview mirrors, and adopting a two-seat layout, with driving entirely controlled by Tesla's autonomous driving software. The Cybercab was unveiled as a concept car at the "We, Robot" event in October 2024, with the first mass-produced vehicle rolling off the line at the Texas Gigafactory in February this year, and mass production starting in April, taking about 18 months from concept to mass production.The vehicle relies on 8 high-definition cameras to perceive the environment and makes driving decisions through an end-to-end neural network, without relying on lidar and high-precision maps. According to Tesla's data, as of September 2026, the global fleet's cumulative assisted driving mileage has exceeded 22.5 billion kilometers. In terms of energy efficiency, it is expected to travel 9.8 kilometers per kilowatt-hour, with an energy consumption of about 10.2 kilowatt-hours per 100 kilometers. The curb weight is 1412 kilograms, equipped with a 47.6 kilowatt-hour battery pack and a 163-kilowatt motor, with a laboratory range of 673 kilometers. After scaling up ride-hailing operations, the cost per mile can be reduced to $0.2, equivalent to about 0.84 yuan per kilometer.The Cybercab will be showcased in multiple cities such as Beijing and Shanghai starting in mid-September. This exhibition does not involve the sale of the vehicle in China, nor does it represent that Tesla will conduct autonomous ride-hailing commercial operations domestically.

first_img Google claims that the cost of AI server memory has exceeded 75%, promoting a dual-track strategy for software and hardware

The SEMICON Taiwan 2026 Memory Summit took place on the 1st, where Nikhil Cherian, Senior Director of Supply Chain Infrastructure at Google Cloud under Alphabet, pointed out that with the popularity of multimodal and mixed expert architectures, AI computation has shifted from being power-limited to memory-limited, with high-performance memory accounting for over 75% of the cost of AI server hardware bill of materials. In the face of capacity, bandwidth, and power consumption bottlenecks, Google is breaking through the AI memory bottleneck through a dual-track strategy of hardware offloading for inference and training, and lossless quantization software algorithms.Google adopts an offloading strategy in hardware architecture, launching TPU 8i for low-latency inference and TPU 8t specialized for large-scale training. The TPU 8i is equipped with 288 GB of high-bandwidth memory, with SRAM capacity on the chip increased threefold to 384 MiB, placing dynamic conversation states and key-value caches on the chip itself to achieve zero chip-off latency. The TPU 8t forms a super-large computing cluster with 9600 chips, achieving a shared pool of HBM at a scale of 2 PB, eliminating chip-off data transfer bottlenecks, along with TPU Direct Storage technology.Google has developed the training-free TurboQuant lossless quantization algorithm, compressing the key-value cache of large models from 32 bits to 3 bits, reducing memory usage by six times without loss of accuracy, resulting in an eightfold acceleration in attention computation, and integrating old-generation DRAM technology to extend the lifecycle of components.

first_img SemiAnalysis: HBF non-HBM alternative, cost and heat dissipation still have uncertainties

P Equity Research and SemiAnalysis researcher Nick Doyle and others discussed high bandwidth flash (HBF) in X Space. Nick stated that it is still too early to determine how much the cost premium of HBF relative to HBM can shrink; existing data mostly comes from vendor claims, such as Sandisk stating that the cost per bit is about one-eighth that of HBM. Yields, testing, and other factors will improve with scale, but structural costs such as TSV, stacking, and pSLC mode will always exist, and durability is a key unknown; if wear exceeds expectations, costs will rise.The application scenarios for HBF are narrow, targeting only AI inference, especially low batch and long context MoE models, and it is not a substitute for HBM. The actual bandwidth target is about 1.6 TB/s, which is at the HBM3E level, suitable for sequential reads to load model weights, more aligned with the capacity needs of a small number of GPUs in local or private enterprises, rather than ultra-large-scale bandwidth scenarios. Heat dissipation reliability has not yet been resolved; flash memory will degrade faster at high temperatures next to GPUs, and mitigation measures such as UCIe separation and daily refresh have yet to be validated.In terms of manufacturing, Sandisk/Kioxia has experience with 3D NAND, and SK Hynix complements HBM-style stacking capabilities, but mass production is still to be confirmed. Overall, storage is shifting towards a specialized layered market, with NAND shortages expected to continue until 2028, and HBF may further impact supply and demand.
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